Related papers: Modeling X-ray photon pile-up with a normalizing f…
We have developed a simulation-based method of spectral analysis for pile-up affected data of X-ray CCDs without any loss of photon statistics. As effects of the photon pile-up appear as complicated nonlinear detector responses, we employ a…
We present X-sifter, a software package designed for near-optimal detection of sources in X-ray images and other forms of photon images in the Poisson-noise regime. The code is based on the Poisson-noise-matched filter (Ofek & Zackay),…
In X-ray astronomy, most observatories utilize multi-pixel photon-counting devices. When a photon counting device observes a bright source, we face an unavoidable problem called pile-up. Pile-up leads to mistakes in the observational…
The eROSITA X-ray telescope on board the Spectrum-Roentgen-Gamma (SRG) satellite has started to detect new X-ray sources over the full sky at an unprecedented rate. Understanding the performance and selection function of the source…
A formalism for treating the pile-up produced in solid-state detectors by laser-driven pulsed x-ray sources has been developed. It allows the direct use of x-ray spectroscopy without artificially decreasing the number of counts in the…
The reliability of detecting source variability in sparsely and irregularly sampled X-ray light curves is investigated. This is motivated by the unprecedented survey capabilities of eROSITA onboard SRG, providing light curves for many…
We present the result of a systematic study of pileup phenomena seen in the X-ray Imaging Spectrometer, an X-ray CCD instrument, onboard the Suzaku observatory. Using a data set of observed sources in a wide range of brightness and spectral…
Ptychography, as an essential tool for high-resolution and nondestructive material characterization, presents a challenging large-scale nonlinear and non-convex inverse problem; however, its intrinsic photon statistics create clear…
We propose a new iterative unfolding method for experimental data, making use of a regularization function. The use of this function allows one to build an improved normalization procedure for Monte Carlo spectra, unbiased by the presence…
Aims: During its all-sky survey phase, the eROSITA X-ray telescope onboard SRG scans through the ecliptic poles every 4 hours. This extensive data set of long-duration, frequent, and consistent observations of thousands of X-ray sources is…
We present a novel approach using neural networks to recover X-ray spectral model parameters and quantify uncertainties, balancing accuracy and computational efficiency against traditional frequentist and Bayesian methods. Frequentist…
We present a rigorous description of the general problem of aperture photometry in high energy astrophysics photon-count images, in which the statistical noise model is Poisson, not Gaussian. We compute the full posterior probability…
The precision of intensity measurements of the extragalactic X-ray Background (XRB) on an angular scale of about a degree is dominated by spatial fluctuations caused by source confusion noise. X-ray source counts at the flux level…
To enhance low-light images to normally-exposed ones is highly ill-posed, namely that the mapping relationship between them is one-to-many. Previous works based on the pixel-wise reconstruction losses and deterministic processes fail to…
Despite their advantages, normalizing flows generally suffer from several shortcomings including their tendency to generate unrealistic data (e.g., images) and their failing to detect out-of-distribution data. One reason for these…
Normalizing Flows (NFs) are flexible explicit generative models that have been shown to accurately model complex real-world data distributions. However, their invertibility constraint imposes limitations on data distributions that reside on…
Mapping the boundary of an extended source is a key step in the study of its morphology. The background contamination and statistical fluctuations of typical astronomical images make this a challenging statistical task, particularly for…
Based on the manifold hypothesis, real-world data often lie on a low-dimensional manifold, while normalizing flows as a likelihood-based generative model are incapable of finding this manifold due to their structural constraints. So, one…
Modeling transformations between arbitrary data distributions is a fundamental scientific challenge, arising in applications like drug discovery and evolutionary simulation. While flow matching offers a natural framework for this task, its…
We study image inverse problems with a normalizing flow prior. Our formulation views the solution as the maximum a posteriori estimate of the image conditioned on the measurements. This formulation allows us to use noise models with…